Hospital-based health technology assessment (HB-HTA) needs to consider all relevant data to help decision-making, including patients’ preferences. In this study, we comprehensively describe the process of identification, refinement and selection of attributes and levels for a discrete choice experiment (DCE).

A mixed-methods design was used to identify attributes and levels explaining low back pain (LBP) patients’ choice for a non-surgical treatment. This design combined a systematic literature review with a patients’ focus group, one-on-one interactions with experts and patients, and discussions with stakeholder committee members. Following the patient's focus group, ranking exercises were conducted. A consensus about the attributes and levels was researched during discussions with committee members.

The literature review yielded 40 attributes to consider in patients’ treatment choice. During the focus group, one additional attribute emerged. The ranking exercises allowed selecting eight attributes for the DCE. These eight attributes and their levels were discussed and validated by the committee members who helped reframe two levels in one of the attributes and delete one attribute. The final seven attributes were: treatment modality, pain reduction, onset of treatment efficacy, duration of efficacy, difficulty in daily living activities, sleep problem, and knowledge about their body and pain.

This study is one of the few to comprehensively describe the selection process of attributes and levels for a DCE. This may help ensure transparency and judge the quality of the decision-making process. In the context of a HB-HTA unit, this strengthens the legitimacy to perform a DCE to better inform decision-makers in a patient-centered care approach.